What is data cataloging, and why is it an increasingly important part of the data management process?
Data cataloging is the process of creating an organized inventory of enterprise data. Data cataloging follows the process of data mapping and uses metadata (which is data that describes or summarizes data) to collect, tag, and store datasets. An organization’s data sets may be stored in a data warehouse, data lake, master data repository, or another storage location like the cloud. Data catalogs are designed to help data workers quickly find the most appropriate data for analytical or business purposes.
Data cataloging solves for several key data management use cases, including data compliance and governance via tools and labeling, data accuracy by standardizing the way data is stored and defined, and data quality through ensuring dependable usage of data elements. Data cataloging also involves the use of search and other adjacent data management techniques and best practices. Data assets most commonly found in data catalogs are structured data, unstructured data, reports and query results, data visualizations and dashboards, machine learning models, and connections between databases.
Data catalogs feature tools for ensuring continuous collection and curation of metadata associated with each data set in order to make assets easier to identify, explore and use in analytic settings. They also enable data set searching by facets, keywords, and business terms. Dataset evaluation is a key component as well, providing users with the ability to preview data sets, see all associated metadata, view user ratings, read user reviews and curator annotations, and understand data quality information.
Data cataloging products come in several shapes and sizes aimed at satisfying various enterprise data management requirements. Tool-specific data catalogs can be packaged as part of a cloud-based data lake, data preparation platforms or Hadoop distributions. There are also data catalogs specifically designed for use in conjunction with data lakes, while enterprise data catalogs should be considered for more general use cases or in environments where collaboration or business-facing use cases are most pressing.
Core capabilities of data cataloging software include the ability to deploy across an enterprise, broad metadata connectivity options, machine learning, automated data lineage, collaboration tools, and embedded data governance and privacy. While standalone tools provide an enterprise hub across the business ecosystem and solution-and-platform-based catalog metadata repositories, machine learning enables the combination of a traditional data management business glossary with data stewardship, data preparation, and data marketplaces.
According to Forrester Research, those currently evaluating data catalogs should consider products that power DataOps, data stewardship, and analytic process automation. Solution-seekers should also consider providers that scale data intelligence and lineage across from metadata to the endpoint.
Evaluating data cataloging software? Start here with this directory of the most popular tools and software to consider.
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